AdaRubric: Task-Adaptive Rubrics for Reliable LLM Agent Evaluation and Reward Learning

Evaluating LLM agent trajectories is fundamentally task-specific: a code-debugging agent should be judged on Correctness and Error Handling, not on Fluency or Safety. Yet the dominant paradigm -- LLM-as-Judge with a fixed rubric -- applies the same static dimensions regardless of task, producing systematic mis-evaluation. We present AdaRubric, a framework that (i) adaptively generates task-specific evaluation rubrics from task descriptions via LLM, (ii) evaluates agent trajectories step-by-step with confidence-weighted, per-dimension scoring, and (iii) produces dense reward signals for preference learning. Three composable filtering strategies, including the novel DimensionAwareFilter tha...

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